Heterogeneous parallel systems incorporate diverse models of parallelism within a single machine or across machines. These systems provide high performance for large, diverse applications. Heterogeneity unfortunately complicates software development. Users currently hand parallelize each task for each architecture. These programs are not portable and must be recoded if changes occur (e.g., machines are added or removed). The user is also burdened with performing the appropriate optimizations to achieve high performance. A compiler can solve these problems. The most important advantages of a compiler based approach are: (1) Portability and Ease of Use: Users program the appropriate model of parallelism for their algorithms. The compiler automatically maps them on to the specifics of the available architectures. (2) High Performance: The compiler optimizes with aggressive, target specific optimizations and coordinates diverse hardware to achieve excellent performance. To manage heterogeneity the compiler must: target diverse architectures, adjust optimization strategies to suit targets, exploit run-time changes in machine availability, and allow languages to evolve. The objective of this research is to investigate a compilation system to support these requirements. The system will use an innovative compiler organization and representations in order to provide the flexibility that heterogeneity requires. This project concentrates on the design and implementation of this compiler infrastructure. The system will be used to investigate automatic parallelization and partitioning strategies for a heterogeneous target. It will address issues such as how to map programmer models of parallelism on to the corresponding machine and how to further optimize it for total system performance. This compiler-based approach will provide an infrastructure capable of performing the advanced dynamic and static optimizations that heterogeneous systems demand for high performa nce. ***
